Job Description
Are you ready to architect the systems that will define the technological landscape of 2026? Nexus Horizon is a premier research lab dedicated to pushing the boundaries of artificial intelligence. We are looking for a visionary Senior AI Architect to lead our next-generation infrastructure initiatives.
In this role, you will not just build models; you will build the foundational logic for autonomous systems that operate at the edge and scale to the cloud. You will work with a world-class team of researchers and engineers to solve complex problems in Natural Language Processing, Computer Vision, and Reinforcement Learning. If you are passionate about the future and possess the technical prowess to turn ambitious concepts into reality, we want to hear from you.
Why Join Us?
- Work on cutting-edge projects that influence global tech standards.
- Competitive compensation package with equity options.
- Flexible work environment in the heart of San Francisco.
- Access to state-of-the-art computing infrastructure.
Responsibilities
- Lead the architectural design and implementation of scalable Machine Learning and Deep Learning systems for 2026-ready platforms.
- Optimize existing AI models for inference speed, latency, and accuracy on diverse hardware architectures.
- Collaborate with cross-functional teams to translate business requirements into technical AI solutions.
- Mentor junior engineers and data scientists, fostering a culture of innovation and continuous learning.
- Stay abreast of the latest advancements in AI research and integrate novel techniques into our production pipelines.
- Ensure robust data governance, security protocols, and ethical AI practices are maintained throughout the development lifecycle.
Qualifications
- PhD or Master’s degree in Computer Science, Mathematics, or a related technical field with a focus on Artificial Intelligence.
- Minimum of 5+ years of professional experience in AI/ML engineering, with at least 2 years in a leadership or architectural capacity.
- Expert proficiency in Python, PyTorch, TensorFlow, or JAX.
- Deep understanding of Large Language Models (LLMs), fine-tuning methodologies, and RAG architectures.
- Strong background in distributed systems, cloud computing (AWS/GCP/Azure), and containerization (Docker/Kubernetes).
- Proven track record of deploying high-impact machine learning models into production environments.